The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide clinical decision support software that assists inpatient providers in improving care for preventable acute inpatient harms, and thereby reduce mortality and morbidity. This grant develops a cloud-based platform that applies machine learning (ML) algorithms in real-time on data extracted from Electronic Health Records (EHRs) and physiologic monitoring devices attached to a patient. The ML tools employed estimate the degree of reliability for each of the data elements as they are collected and integrates these signals to provide an accurate, individualized risk estimate of patient health over time in order to best guide patient treatment and allocation of hospital resources. Our initial target condition is sepsis, one of the most costly and most deadly diseases in hospitals. This grant develops an end-to-end system to provide risk assessment and implementation of timely treatment. For commercial potential, the underlying core technology can be extended to other clinical scenarios. The proposed project enables scaling of high-precision state-of-the-art Bayesian machine learning techniques that forecast the chance of acute deterioration. This includes tackling the challenges in scaling this machine learning system to function across many care providers, patients, and hospitals. To achieve these goals, this project will develop new methods for running machine learning algorithms in a distributed fashion in cloud computing settings, especially in distinguishing where multiple machines need to coordinate, and arguably more importantly, where they can avoid coordinating in training on data. Further, the project develops software to provide information back to providers so as to enable interventions that can alter patient trajectory. Here the software will encompass how to best use the resulting inferences in guiding care.